Clinical report retrieval and/or comparison

ABSTRACT

Instructions ( 108 ) cause a processor ( 104 ) to: classify a clinical report for a subject under evaluation by one of anatomical organ or disease; identify and retrieve clinical reports for the same subject from the healthcare data source(s); group the retrieved clinical report by one of anatomical organ or disease; select a group of the clinical report, wherein the group includes reports for a same or related one of the anatomical organ or the disease; build a model that predicts semantic relationships between nodes in the reports in the selected group of reports based on one or more of extracted parameters or keywords; compare one of the parameter values or the keywords across the reports using the model; construct a graphical timeline of the reports; highlight differences in the parameter values or the keywords based on a result of the compare; and visually present the graphical timeline with the highlighted differences.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§ 371 of International Application No. PCT/EP2017/060478, filed on May3, 2017, which claims the benefit of U.S. Provisional Application No.62/336,779, filed May 16, 2016. These applications are herebyincorporated by reference herein, for all purposes.

FIELD OF THE INVENTION

The following generally relates to clinical report retrieval and/orcomparison.

BACKGROUND OF THE INVENTION

Clinical text reports describe details from clinical processes such asadmission, discharge, routine ward rounds, imaging studies, andlaboratory reports investigations. These reports contain different typesof information on patient scenarios (e.g., diagnoses, treatment plans,prognosis, etc.), including unstructured patient details with valuablecontextual insight into the past and current health scenarios, andstructured data, which has high fidelity and is typically measuredperiodically. Structured values (measurements) generally allowclinicians to make prompt assessments of patient states towardsappropriate interventions. In addition, such values can be used todetermine treatment efficacy and predict the effectiveness of futureinterventions. Understanding patient scenarios described in clinicalreports and interpreting structured data (values) in context ofunstructured details within the same reports and other related reportsfor a specific patient facilitates quality healthcare.

Electronic medical records (EMRs) and patient dashboards notifyclinicians when a new report has been created. EMR systems offerfunctionalities to access clinical reports via queries to largedatabases. In some systems, the user interface includes hyperlinks(representing database queries) through which clinical reports can beaccessed. Clinical reports resulting from such queries are presented astext files or scanned documents to users who subsequently review andinterpret the contents. Manually reviewing and interpreting clinicalreports is often time consuming and prone to human errors. Furthermore,accessing archived clinical reports in most EMR systems can be verychallenging due to technical bottlenecks, and such systems often do notprovide functionalities to automatically compare longitudinal reports toextract clinically relevant connections that can inform clinicians onoverall patient acuity and support clinical decision making.

SUMMARY OF THE INVENTION

Aspects of the present application address the above-referenced mattersand others.

According to one aspect, a system includes a healthcare data source(s)and a computing system with a memory device configured to storeinstructions, including a clinical report retrieval and/or comparisonmodule. The processor that executes the instructions, which causes theprocessor to: classify a clinical report for a subject under evaluationby one of anatomical organ or disease; identify and retrieve clinicalreports for the same subject from the healthcare data source(s); groupthe retrieved clinical reports by one of anatomical organ or disease;select a group of the clinical report, wherein the group includesreports for a same or related one of the anatomical organ or thedisease; build a model that predicts semantic relationships between thereports in the selected group of reports based on one or more ofextracted parameters or keywords; compare one of the parameter values orthe keywords across the reports using the model; construct a graphicaltimeline of the reports; highlight differences in the parameter valuesor the keywords based on a result of the compare; and visually presentthe graphical timeline with the highlighted differences.

In another aspect, a method includes classifying, with a processor, aclinical report for a subject under evaluation by one of anatomicalorgan or disease, identifying and retrieving, with the processor,clinical reports for the same subject from the healthcare datasource(s), and grouping, with the processor, the retrieved clinicalreports by one of anatomical organ or disease, and selecting, with theprocessor, a group of the clinical report, wherein the group includesreports for a same or related one of the anatomical organ or thedisease. The method further includes building, with the processor, amodel that predicts semantic relationships between the reports in theselected group of reports based on one or more of extracted parametersor keywords, comparing, with the processor, one of the parameter valuesor the keywords across the reports using the model, constructing, withthe processor, a graphical timeline of the reports, highlighting, withthe processor, differences in the parameter values or the keywords basedon a result of the compare, and visually presenting, with the processor,the graphical timeline with the highlighted differences.

In another aspect, a non-transitory computer readable medium is encodedwith computer executable instructions, which, when executed by aprocessor of a computer, cause the computer to: classify a clinicalreport for a subject under evaluation by one of anatomical organ ordisease, identify and retrieve clinical reports for the same subjectfrom the healthcare data source(s), group the retrieved clinical reportsby one of anatomical organ or disease, select a group of the clinicalreport, wherein the group includes reports for a same or related one ofthe anatomical organ or the disease, build a model that predictssemantic relationships between the reports in the selected group ofreports based on one or more of extracted parameters or keywords,compare one of the parameter values or the keywords across the reportsusing the model, construct a graphical timeline of the reports,highlight differences in the parameter values or the keywords based on aresult of the compare, and visually present the graphical timeline withthe highlighted differences.

Still further aspects of the present invention will be appreciated tothose of ordinary skill in the art upon reading and understand thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 schematically illustrates an example computing system with aclinical report retrieval and/or comparison module.

FIG. 2 schematically illustrates an example of the clinical reportretrieval and/or comparison module.

FIG. 3 illustrates an example flow chart for clinical report retrievaland/or comparison.

FIG. 4 schematically illustrates an example supervised approach tobuilding a semantic relationship network.

FIG. 5 schematically illustrates an example semi-supervised approach tobuilding a semantic relationship network.

FIG. 6 shows an example in which parameter values are shown in atimeline.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates an example system 100. The system 100 includes acomputing system 102 with at least one processor 104 (e.g., amicroprocessor, a central processing unit, etc.) that executes at leastone computer readable instruction stored in a computer readable storagemedium (“memory”) 106, which excludes transitory medium and includesphysical memory and/or other non-transitory medium. The instruction, inthis example, includes a clinical report retrieval and/or comparisonmodule 108 with corresponding computer executable instructions. Thecomputing system 102 also includes output device(s) 110, such as adisplay monitor, portable memory, a network interface, etc., and aninput device(s) 112 such as a mouse, keyboard, a network interface, etc.One or more healthcare data sources 114 stores data such aselectronically formatted clinical reports.

In one non-limiting example, the instructions of the clinical reportretrieval and/or comparison module 108, when executed by the at leastone processor 104, cause the at least one processor 104 to retrieverelevant longitudinal reports from the one or more healthcare datasources 114 and/or compare certain patient details in these reports togenerate clinically relevant semantic information networks. Comparing acurrent report with an older report of the same type (e.g. current andpast EKG reports) is useful in understanding how the patient conditionhas changed over time. In addition, the current report can be comparedto other related reports such as comparing the current EKG report to aprevious echocardiogram (echo) report. Such comparisons can assistclinicians in constructing a model of patient scenarios towards betterunderstanding of causal relationships, patient acuity, potentialtreatment options, intervention effectiveness, and prognosis.

An example of the clinical report retrieval and/or comparison module 108is shown in FIG. 2 and includes a report classification (or first)sub-module 202, a retrieval of similar and related reports (or second)sub-module 204, an extract reports features and predict relationshipbetween reports (or third) sub-module 206, and a report analysis,test/procedure predicting effectiveness and recommendation (or fourth)sub-module 208. The first sub-module 202 classifies a new report basedon report type and/or the body system it relates to (e.g. an echo reportrelates to the cardiovascular system). The second sub-module 204 findsrelated reports for the selected patient from the healthcare datasource(s) 114. The third sub-module 206 extracts quantitative(structured) information along with contextual (unstructured) detailsfrom the retrieved reports and identifies the semantic relationshipsacross the reports. The fourth sub-module 208 presents the extracted theclinically relevant semantic information and relationships betweenreports to the user. In one instance, results are visualized and/or usedto predict test effectiveness and/or recommend next steps.

A challenge in finding related reports is that different types ofreports (labs, imaging studies, procedure notes etc.) might relate tothe same issue, and these relationships are not explicit. Relative toretrieving previous reports of the same type for the same patient,retrieving different but related reports (e.g. a lab report and animaging report to assess renal function) requires an understanding ofthe issue as well as analyses of the report contents. Hence, to solvethis problem the clinical report retrieval and/or comparison moduleemploys an algorithm that can learn the relationships between reportsand use that information to predict the relatedness of reports. Machinelearning techniques (e.g. Bayesian networks, random forest, supportvector machines, etc.) can be used to build models which can learnrelationships across various reports. The model would be trained onclinical concepts in the reports and standard clinical ontologies (e.g.the Systematized Nomenclature of Medicine—Clinical Terms, or SNOMED CT)as input features and output the predicted relationship across reports.

A challenge in comparing across various reports is understanding thecontent of the reports and putting them in a temporal context. Sincethese reports are typically semi-quantitative, a step in processing thecontent of the reports is to identify and extract these structured dataalong with the unstructured (descriptive) information of the patientscenario. Similar structured data types extracted from different reportscan be compared to generate trend reports. To compare different datatypes describing a particular patient scenario, the semanticrelationships identified in the reports would be used to generate acontextual interpretation of the clinical picture presented by thepatient's condition towards better informed clinical decision-making. Atechnical challenge which can be mitigated by the clinical reportretrieval and/or comparison module includes identification of semanticrelationships across reports and automatically categorizing/ranking suchrelationships based on clinical importance.

FIG. 3 schematically illustrates a flow chart 300 for clinical reportretrieval and/or comparison. The first and last acts 302 and 322 areperformed via a front end user interface, and the acts 304-320 therebetween are performed via a backend machine learning, which learns fromprevious reports with known relationships clinically relevant semanticinformation.

At 302, a user selects a clinical report of interest for a patient. Theclinical report is in electronic format and stored in computer memorysuch as the memory 106, a healthcare data source 114, and/or othermemory. The clinical report can be selected using the computing system102 via a mouse pointer from a list of available reports presented tothe user on a display monitor of the output device(s) 110, by typing viaa keyboard of the computing system 102 a file name at a command prompt,and/or other known technique. The clinical report may be a most recentor other report for the patient.

At 304, the computing system 102 classifies the selected report. Thiscan be done by using a report type information (e.g., SNOMED CT) codefor the report type and final diagnosis and/or information from a reportheader and/or metadata, which has information on the report source, typeand/or other details. The classification categorizes the report based onbody system and/or as related to a particular disease. Otherclassifications are contemplated herein.

At 304 is performed by the report classification (or first) sub-module202 of the clinical report retrieval and/or comparison module 108.

At 306, the computing system 102 retrieves related archived reports forthe patient from the healthcare data source(s) 114. For example, in onenon-limiting instance, the computing system 102 accesses a health caredata source of the healthcare data source(s) 114 such as a reportdatabase of a hospital and retrieves all reports for the selectedpatient using a unique medical record number (MRN) and/or otheridentifier unique to the patient.

At 308, the computing system 102 groups each retrieved archived report.For example, in one non-limiting instance, the retrieved archivedreports are classified using the approach described in act 304 and/orother approach into either body system-based groups or disease-basedgroups. Other groupings are also contemplated herein.

At 310, same type and/or related reports from the grouped reports areselected. To select a same type of report, the computing system 102 usesreport type information (e.g. SNOMED CT codes) to find an exact match.Related reports are broadly defined as previous reports belonging to asame body system and/or referring to a same disease. To select a relatedreport, the computing system 102 matches the body system and/or diseasetype information to the relevant information from the selected report.

Acts 306, 308 and 310 are performed by the retrieval of similar andrelated reports (or second) sub-module 204 of the clinical reportretrieval and/or comparison module 108.

At 312, the computing system 102 extracts parameters and/or keywordsfrom the selected retrieved reports. In one instance, this includesextracting quantitative information (e.g., measurements, lab values,etc.) in these reports as well as context of such values with respect tothe patient scenario. A natural language processing (NLP) pipeline canbe used to extract key structured and unstructured information.

At 314, the computing system 102 determines a semantic relationshipnetwork, which will connect the various reports selected. FIGS. 4 and 5illustrated two non-limiting approaches to building a semanticrelationship network. Other approaches are also contemplated herein.

FIG. 4 schematically illustrates an approach based on supervised machinelearning that trains on corpora of annotated reports indicated semanticconcept relationships. To build this using a machine learning model forgenerating the semantic relationship network using supervised learning,a network of anatomical and physiological concepts in clinical reportsare manually generated by domain experts. From an existing parent-childrelationship in the standard clinical ontologies (e.g. SNOMED CT tree),clinical domain experts would manually develop a network by linkingrelated child nodes from various parent nodes to create new edges in asemantic graph.

In FIG. 4 , N1 represents the cardiovascular system, N2 represents therespiratory system, N3 represents the renal system, . . . . The nodesdirectly under the organ systems N1, N2, N3, . . . are respectfullynumbered N1.1, N1.2 . . . , N2.1, N2.2 . . . , N3.1, N3.2 . . . . Thenodes directly under the N1.1, N1.2 . . . , N2.1, N2.2 . . . , N3.1,N3.2 . . . are respectfully numbered N1.1.1, . . . , N1.2.1, . . . ,N2.1.1, . . . , N2.2.1, . . . , N3.1.1, . . . , N3.2.1 . . . andindicate a hierarchical relationship. The ontology is shown in FIG. 4 asa tree structure in solid lines. The dashed lines represent the manuallylinked nodes.

The anatomical and physiological concept networks can then be used toidentify the relationship between reports. Keywords and clinicalconcepts extracted from the reports by the NLP pipeline would beconnected based on the concept networks. The relationship identified bythe concept networks would be used to semantically link the reports andcontextualize the contents for better understanding of the patient'soverall clinical picture.

FIG. 5 schematically illustrates another approach which is based onsemi-supervised learning. In this approach, the computing system 102initially learns the semantic relationships across clinical reports 500in an unsupervised manner 502. The input 504 features to the computingsystem 102 are the NLP extracted structured and unstructuredinformation. In addition, features obtained from the report header suchas body/organ system information, specific anatomical or physiologicaldetails, the type of report (e.g. type of imaging study or lab report orprocedure report) and/or a date of a report are included. Anotherfeature would be the relationship between the different anatomical orphysiological ontologies.

All this information will be used as features to build a conceptrelationship model across reports. An example, first pass 506 usesarrows to show relationships between reports. The results of thisinitial unsupervised learning would be presented to the clinical domainexperts via a display monitor output device 110, as shown at 508, andthe expert will evaluate the accuracy of the semantic relationships. Inthe illustrated example, the user removes a relationship, which is shownthrough an “X” under the arrow between the top two reports. Based on theexperts' evaluation, the computing system 102 will adjust via a modelrecalibration 510 the network parameters to develop a more accuratenetwork 512 of concepts and reports. This model can now be used toidentify new semantic relationships across other groups of reports.

The computing system 102 will learn over time with more data and furtherrefine the concept network, e.g., via a feedback 514. Moreover, based onthe knowledge corpus of the computing system 102, computing system 102can generate new hypothesis regarding previously unknown conceptrelationships. Based on the extracted concepts and learnt semanticrelationships, a distance score will be calculated to measure therelatedness of the retrieved reports to the initially selected report.The reports would then be ranked and filtered based on how far they arefrom the initial report.

For example, in one instance the content (keywords) of the user-selectedreport is compared to a set of keywords in all candidate relatedreports, and a mean distance score is computed based on how semanticallysimilar the set of keywords for each candidate report is to the contentof the original report. The lower the mean distance score, the more thelikelihood of a candidate report being similar to the original report.The mean distance score is then used to rank all related reports.Additionally, any report with a distance score above a value of anempirical distance score threshold is considered remotely related to theoriginal report and vice versa.

Returning to FIG. 3 , acts 312 and 314 are performed by the extractreports features and predict relationship between reports (or third)sub-module 206 of the clinical report retrieval and/or comparison module108.

At 316, an effectiveness is predicted with report information history.For example, in one non-limiting instance for each report on atest/imaging study/procedure etc. computing system 102 finds theeffectiveness of that test/imaging study/procedure from previouslypublished studies from an external publications database. The computingsystem 102 would then display the sensitivity and specificity values forthat particular test in detecting the condition of interest. This wouldhelp the physician make informed decisions on appropriate investigativeprocedures for specific clinical scenarios. Optionally, the cache ofreport relationships can be used to provide information on procedureeffectiveness based on published literature.

At 318, tests and/or procedures are recommended based on previouspredictions. For example, in one non-limiting instance the computingsystem 102 recommends a most optimal next steps for a given patientscenario. The computing system 102 would cache the semantic relationshipbetween reports from different searches towards building a corpus ofconnected reports. When the user wants recommendations on the next step,the system can show a list of most relevant tests/procedures based onthese cached networks of semantic information. Optionally, the cache ofreport relationships can be used to recommend most effectiveinvestigative procedure for the patient given previous reports andpublished literature.

At 320, the parameter values and/or keywords are compared across reportsin a timeline identifying any changes. For example, in one non-limitinginstance the identified semantic relationships (from the previouscomponent) and the report timestamps are used to order the reports in ameaningful temporal manner. Certain concepts and their semanticrelationships would be used to generate a brief summary of the patientscenario. This summary would be presented to the user along with thegroup of related reports with the relevant sections highlighted.

For reports of the same type, parameter values can be shown in atimeline and/or a trend graph. FIG. 6 shows an example in whichparameter values are shown in a timeline. For this example, ejectionfraction information is extracted from multiple echo reports of thepatient and graphed over time. The shaded portion of the graph indicatesreduced ejection fraction. The dashed line in the graph indicatesintervention to improve ejection fraction.

Acts 316, 318 and 320 are performed by the report analysis,test/procedure predicting effectiveness and recommendation (or fourth)sub-module 208 of the clinical report retrieval and/or comparison module108.

At 322, the results are visually presented via a display monitor of theoutput device(s) 110. The displayed data presents a comparison acrossreports and highlights certain findings related to the comparison.

The following provides a non-limiting example use case. The example usecase is for evaluating the effectiveness of treatment for a pancreatictumor. An abdominal x-ray of a patient with prolonged constipationshowed multiple ‘air-fluid levels’. Further investigation via anabdominal CT scan revealed a pancreatic mass obstructing the second partof the duodenum. A Whipple procedure (pancreatoduodenectomy) wasperformed to remove the tumor. Postoperative abdominal X-ray revealed noair fluid levels. Subsequent abdominal CT also showed no sign of themass.

When a clinician accessed the postoperative abdominal CT report via thecomputing system 102 to review the patient history related to thisreport, the computing system 102 retrieved the above mentioned X-ray andCT reports along with the procedure notes. The computing system 102arranged the reports in a timeline and extracted the relevant concepts.The parameters in the two X-ray reports and two CT reports werecompared. The changes in certain findings in these reports werehighlighted. In addition, quantitative data with multiple values weregraphed.

The method herein may be implemented by way of computer readableinstructions, encoded or embedded on computer readable storage medium,which, when executed by a computer processor(s), cause the processor(s)to carry out the described acts. Additionally, or alternatively, atleast one of the computer readable instructions is carried by a signal,carrier wave or other transitory medium.

The approach described herein can improve computing system performance.For example, the computing system 102 can store the retrieved reportsand/or semantic relationship in cache memory. Subsequently, if the samereport is selected again, e.g., by a different clinician, etc., thecomputing system 102 can automatically retrieve the related reportedand/or the semantic relationship stored in the cache memory, which canbe part of the memory 106 and/or memory. This reduces the processingcycles required to retrieve these reports and/or determine the semanticrelationship relative to having to identify and retrieve these reportsand again determine the semantic relationship. In other words, it canreduce the number of processing cycles required to construct ameaningful output.

A result of the approach described herein can also drive another device.For example, the computing system 102 can transmit a signal indicativeof the semantic relationship to another device, which causes the otherdevice to retrieve and return and/or display a suitable clinicalprotocol stored in a protocol database. This clinical protocol may bedifferent to the current clinical protocol being followed. Without thistransmission controlling the other device, the original protocol wouldstill be followed. In one instance, the other protocol includes an actperformed by a machine, the transmission causes the device performingthat act to perform the act. For example, the transmission may cause adevice in a laboratory to perform another test on a sample beingprocessed.

The invention has been described herein with reference to the variousembodiments. Modifications and alterations may occur to others uponreading the description herein. It is intended that the invention beconstrued as including all such modifications and alterations insofar asthey come within the scope of the appended claims or the equivalentsthereof.

What is claimed is:
 1. A system, comprising: a healthcare data source;and a computing system, comprising: a memory device configured to storeinstructions a processor that executes the instructions, which cause theprocessor to: classify a clinical report, for a subject underevaluation, by one of an anatomical organ or disease; identify andretrieve clinical reports for the same subject from the healthcare datasource; group the retrieved clinical reports by one of anatomical organor disease; select a group of the clinical reports, wherein the groupincludes reports for a same or related one of the anatomical organ orthe disease; build a model that predicts semantic relationships betweennodes in the reports in the group of clinical reports based on one ormore of extracted parameters or keywords; compare one of the one or moreextracted parameters or the keywords across the reports using the model;construct a graphical timeline of the reports; highlight difference invalues of the one or more extracted parameters or the keywords based ona result of the compare; identify one or more tests that can be appliedto the subject based on the one or more of the extracted parameters orthe keywords; determine a sensitivity value and a specificity value ofthe at least one of the one or more identified tests; visually presentthe comparison of the one or more extracted parameters or keywords andthe highlighted differences related to the comparison in the graphicaltimeline; and visually present the sensitivity value and the specificityvalue.
 2. The system of claim 1, wherein instructions further cause theprocessor to use a supervised machine learning approach or asemi-supervised learning approach to build the model.
 3. The system ofclaim 2, wherein: the supervised machine learning approach is trainedwith a corpora of annotated reports with known semantic relationships;and the instructions further cause the processor to link a network ofanatomical and physiological concepts across the clinical report basedon the known semantic relationships.
 4. The system of claim 3, whereinthe instructions further cause the processor to predict semanticrelationships between the reports using the link between the network ofanatomical and physiological concepts and the clinical report.
 5. Thesystem of claim 2, wherein the instructions further cause the processorto build the model based on one or more of extracted structured andunstructured information from one or more of body/organ systeminformation obtained from a report header, specific anatomical orphysiological details, a type of report, or a relationship betweendifferent anatomical or physiological ontologies.
 6. The system of claim5, wherein the instructions further cause the processor to visuallypresent the model; receive feedback about a correctness of the semanticrelationships; and update the model based on the feedback, andconstructs a final model based on the model.
 7. The system of claim 5,wherein the instructions further cause the processor to build the modelbased on previously received feedback.
 8. The system of claim 1, whereinthe instructions further cause the processor to predict an effectivenessof the model based on report information history from previouslypublished studies from an external publications database.
 9. The systemof claim 1, wherein the instructions further cause the processor torecommend at least one of a test or a procedure based on previouspredictions through a list of most relevant tests or procedures based onthe semantic relationships.
 10. The system of claim 1, wherein theinstructions further cause the processor to: transmit a signalindicative of the graphical timeline with the differences to a device,which causes the device to retrieve and return or present a suitableclinical protocol for the subject; or perform a processing act on asample for the subject.
 11. The system of claim 1, wherein thesemantical relationships are stored in a memory and the processor isadapted to use the semantic relationships instead of building anothermodel in response to a different evaluation for the same subject by adifferent entity.
 12. A method, comprising: classifying, with aprocessor, a clinical report for a subject under evaluation by one ofanatomical organ or disease; identifying and retrieving, with theprocessor, clinical reports for the same subject from one or morehealthcare data sources; grouping, with the processor, the clinicalreports by one of an anatomical organ or disease; selecting, with theprocessor, a group of the clinical reports, wherein the group includesreports for a same or related one of the anatomical organ or thedisease; building, with the processor, a model that predicts semanticrelationships between nodes of ontology branches in the reports in thegroup of clinical reports based on one or more of extracted parametersor keywords; comparing, with the processor, values from one or more ofthe extracted parameters or the keywords across the reports using themodel; constructing, with the processor, a graphical timeline of thereports; highlighting, with the processor, differences in the valuesfrom the one or more of the extracted parameters or the keywords basedon a result of the compare; identifying, with the processor, one or moretests that can be applied to the subject based on the one or more of theextracted parameters or the keywords; determining a sensitivity valueand a specificity value of the at least one of the one or moreidentified tests; visually presenting, with the processor, thecomparison of the one or more extracted parameters or keywords and thehighlighted differences related to the comparison in the graphicaltimeline; and visually presenting, with the processor, the sensitivityvalue and the specificity value.
 13. The method of claim 12, whereinbuilding the model includes predicting the semantic relationshipsbetween known links between a network of anatomical and physiologicalconcepts and the clinical report; and wherein the method furthercomprises: creating the links through supervised machine learning with acorpora of annotated reports with known semantic relationships.
 14. Themethod of claim 12, further comprising: building an initial model basedon one or more of extracted structured and unstructured information;visually presenting the initial model; receiving feedback about thesemantic relationships in the initial model; and changing the semanticrelationships in the initial model based on the feedback, which buildsthe model.
 15. A tangible, non-transitory computer readable mediumencoded with computer executable instructions, which, when executed by aprocessor of a computer, cause the computer to: classify a clinicalreport for a subject under evaluation by one of anatomical organ ordisease; identify and retrieve clinical reports for the same subjectfrom a healthcare data source; group the retrieved clinical reports byone of an anatomical organ or disease; select a group of the clinicalreports, wherein the group includes reports for a same or related one ofthe anatomical organ or the disease; build a model that predictssemantic relationships between the reports in the group of reports basedon one or more of extracted parameters or keywords; compare values ofthe one or more extracted parameters or the keywords across the reportsusing the model; construct a graphical timeline of the reports;highlight differences in the values of the one or more extractedparameters or the keywords based on a result of the compare; identifyone or more tests that can be applied to the subject based on the one ormore of the extracted parameters or the keywords; determine asensitivity value and a specificity value of the at least one of the oneor more identified tests; visually present the comparison of the one ormore extracted parameters or keywords and the highlighted differencesrelated to the comparison in the graphical timeline; and visuallypresent the sensitivity value and the specificity value.
 16. Thetangible, non-transitory computer readable medium of claim 15, whereinthe instructions further cause the processor to use a supervised machinelearning approach or a semi-supervised learning approach to build themodel.
 17. The tangible, non-transitory computer readable medium ofclaim 16, wherein the supervised machine learning approach is trainedwith a corpora of annotated reports with known semantic relationships;and the instructions further cause the processor to link a network ofanatomical and physiological concepts across the clinical report basedon the known semantic relationships.
 18. The tangible, non-transitorycomputer readable medium of claim 17, wherein the instructions furthercause the processor to predict semantic relationships between theclinical report using the link between the network of anatomical andphysiological concepts and the clinical reports.
 19. The tangible,non-transitory computer readable medium of claim 16, wherein theinstructions further cause the processor to build the model based on oneor more of extracted structured and unstructured information from one ormore of body/organ system information obtained from a report header,specific anatomical or physiological details, a type of report, or arelationship between different anatomical or physiological ontologies.20. The tangible, non-transitory computer readable medium of claim 19,wherein the instructions further cause the processor to visually presentthe model; receive feedback about a correctness of the semanticrelationships; and update the model based on the feedback, andconstructs a final model based on the model.